{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-26T12:53:37.425043700Z",
     "start_time": "2024-05-26T12:53:36.010729500Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.decomposition import PCA\n",
    "import jieba\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 1.数据中含有字符串的时候，如何做特征抽取"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "['city=上海' 'city=北京' 'city=深圳' 'temperature']\n",
      "--------------------------------------------------\n",
      "  (0, 1)\t1.0\n",
      "  (0, 3)\t100.0\n",
      "  (1, 0)\t1.0\n",
      "  (1, 3)\t60.0\n",
      "  (2, 2)\t1.0\n",
      "  (2, 3)\t30.0\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "[[  0.   1.   0. 100.]\n",
      " [  1.   0.   0.  60.]\n",
      " [  0.   0.   1.  30.]]\n",
      "--------------------------------------------------\n",
      "[{'city=北京': 1.0, 'temperature': 100.0}, {'city=上海': 1.0, 'temperature': 60.0}, {'city=深圳': 1.0, 'temperature': 30.0}]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def dictvec():\n",
    "    \"\"\"\n",
    "    字典数据抽取\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # 实例化\n",
    "    # sparse改为True,输出的是每个不为零位置的坐标，稀疏矩阵可以节省存储空间\n",
    "    # 矩阵中存在大量的0，sparse存储只记录非零位置，节省空间的作用\n",
    "\n",
    "    # Vectorizer是矢量器\n",
    "    dict = DictVectorizer(sparse=True)  # 把sparse改为True看看\n",
    "\n",
    "    #每个样本都是一个字典，有三个样本\n",
    "    data = dict.fit_transform(\n",
    "        [{'city': '北京', 'temperature': 100},\n",
    "         {'city': '上海', 'temperature': 60},\n",
    "         {'city': '深圳', 'temperature': 30}]\n",
    "    )\n",
    "\n",
    "    # 字典中的一些类别数据，分别进行转换成特征\n",
    "    print(dict.get_feature_names_out()) # ['city=上海' 'city=北京' 'city=深圳' 'temperature']\n",
    "    print('-' * 50)\n",
    "\n",
    "    # 三行四列的矩阵\n",
    "    # 因为 city 被拆分成了'city=上海' 'city=北京' 'city=深圳' 三个特征\n",
    "    print(data) # 稀疏矩阵\n",
    "    print(type(data))\n",
    "    print(data.toarray()) # ndarray\n",
    "    print('-' * 50)\n",
    "\n",
    "\n",
    "    # 去看每个特征代表的含义，逆转回去\n",
    "    print(dict.inverse_transform(data))\n",
    "    # [\n",
    "    # {'city=北京': 1.0, 'temperature': 100.0},\n",
    "    # {'city=上海': 1.0, 'temperature': 60.0},\n",
    "    # {'city=深圳': 1.0, 'temperature': 30.0}\n",
    "    # ]\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "dictvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-26T11:56:35.033989Z",
     "start_time": "2024-05-26T11:56:34.977142700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2 一段英文文本如何变为数值类型"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['is' 'life' 'python' 'short']\n",
      "--------------------------------------------------\n",
      "  (0, 1)\t2\n",
      "  (0, 0)\t1\n",
      "  (0, 3)\t1\n",
      "  (0, 2)\t1\n",
      "  (1, 1)\t1\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (2, 1)\t1\n",
      "  (2, 0)\t1\n",
      "  (2, 3)\t1\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n",
      "--------------------------------------------------\n",
      "[[1 2 1 1]\n",
      " [1 1 1 0]\n",
      " [1 1 0 1]]\n",
      "--------------------------------------------------\n",
      "[array(['life', 'is', 'short', 'python'], dtype='<U6'), array(['life', 'is', 'python'], dtype='<U6'), array(['life', 'is', 'short'], dtype='<U6')]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def couvec():\n",
    "    # 实例化CountVectorizer\n",
    "    # max_df, min_df整数：所有文档中，出现次数小于min的词会被丢弃。如果出现该词的文档数量大于max，丢弃该词\n",
    "    # max_df, min_df小数：某个词的出现的次数／所有文档数量\n",
    "    # min_df=2\n",
    "    # 默认会去除单个字母的单词，默认认为这个词对整个样本没有影响,认为其没有语义\n",
    "    vector = CountVectorizer(min_df=2)\n",
    "\n",
    "    # 调用fit_transform输入并转换数据\n",
    "    res = vector.fit_transform(\n",
    "        [\"life is  short,i like python life\",\n",
    "         \"life is too long,i dislike python\",\n",
    "         \"life is short\"]\n",
    "    )\n",
    "\n",
    "\n",
    "    # 以逗号or空格，分割每个词\n",
    "    # 不区分大小写\n",
    "    print(vector.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "\n",
    "    # 根据min=2，最终只留下4个词，所以3行4列的矩阵\n",
    "    print(res)\n",
    "    print(type(res))    # res是稀疏矩阵\n",
    "    print('-'*50)\n",
    "\n",
    "    # 对照feature_names，标记每个词出现的次数\n",
    "    print(res.toarray())\n",
    "    print('-'*50)\n",
    "\n",
    "    #拿每个样本里的特征进行显示\n",
    "    print(vector.inverse_transform(res))\n",
    "\n",
    "\n",
    "couvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-08T08:13:11.245980700Z",
     "start_time": "2024-05-08T08:13:11.214438400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 一段汉字文本如何数值化，对于汉字不能用空格来分割"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['python' '不用' '人生漫长' '人生苦短' '喜欢']\n",
      "--------------------------------------------------\n",
      "  (0, 3)\t1\n",
      "  (0, 4)\t1\n",
      "  (0, 0)\t2\n",
      "  (1, 0)\t1\n",
      "  (1, 2)\t1\n",
      "  (1, 1)\t1\n",
      "--------------------------------------------------\n",
      "[[2 0 0 1 1]\n",
      " [1 1 1 0 0]]\n",
      "--------------------------------------------------\n",
      "[array(['人生苦短', '喜欢', 'python'], dtype='<U6'), array(['python', '人生漫长', '不用'], dtype='<U6')]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def countvec():\n",
    "    \"\"\"\n",
    "    对文本进行特征值化,不统计，因为单个汉字字母没有意义,不统计\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    cv = CountVectorizer()\n",
    "\n",
    "    data = cv.fit_transform([\"人生苦短，我 喜欢 python python\", \"人生漫长，不用 python\"])\n",
    "\n",
    "    print(cv.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "\n",
    "    print(data) #稀疏存储，只记录非零位置\n",
    "    print('-'*50)\n",
    "\n",
    "    print(data.toarray())\n",
    "    print('-'*50)\n",
    "\n",
    "    print(cv.inverse_transform(data))\n",
    "    return None\n",
    "\n",
    "\n",
    "countvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-08T08:13:21.213588800Z",
     "start_time": "2024-05-08T08:13:21.198918100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1.3 掌握如何对中文进行分词"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n"
     ]
    },
    {
     "data": {
      "text/plain": "('今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。',\n '我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。',\n '如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。')"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "def cutword():\n",
    "    \"\"\"\n",
    "    通过jieba对中文进行分词\n",
    "    三步走：cut、list、join\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    con1 = jieba.cut(\"今天很残酷，明天更残酷，后天很美好，但绝大部分是死在明天晚上，所以每个人不要放弃今天。\")\n",
    "\n",
    "    con2 = jieba.cut(\"我们看到的从很远星系来的光是在几百万年之前发出的，这样当我们看到宇宙时，我们是在看它的过去。\")\n",
    "\n",
    "    con3 = jieba.cut(\"如果只用一种方式了解某样事物，你就不会真正了解它。了解事物真正含义的秘密取决于如何将其与我们所了解的事物相联系。\")\n",
    "\n",
    "    # print(type(con1))   # 生成器类型 <class 'generator'>\n",
    "    # print('-' * 50)\n",
    "\n",
    "    # 把生成器转换成列表\n",
    "    content1 = list(con1)\n",
    "    content2 = list(con2)\n",
    "    content3 = list(con3)\n",
    "    print(content1)\n",
    "    print(content2)\n",
    "    print(content3)\n",
    "\n",
    "    # 把列表转换成字符串，每个词之间用空格隔开\n",
    "    c1 = ' '.join(content1) # str\n",
    "    c2 = ' '.join(content2)\n",
    "    c3 = ' '.join(content3)\n",
    "\n",
    "    return c1, c2, c3\n",
    "\n",
    "cutword()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-26T12:53:41.348981200Z",
     "start_time": "2024-05-26T12:53:41.301833100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。\n",
      "我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。\n",
      "如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。\n",
      "--------------------------------------------------\n",
      "['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个' '看到'\n",
      " '真正' '秘密' '绝大部分' '美好' '联系' '过去' '这样']\n",
      "--------------------------------------------------\n",
      "  (0, 6)\t2\n",
      "  (0, 25)\t2\n",
      "  (0, 21)\t2\n",
      "  (0, 12)\t1\n",
      "  (0, 31)\t1\n",
      "  (0, 30)\t1\n",
      "  (0, 23)\t1\n",
      "  (0, 18)\t1\n",
      "  (0, 26)\t1\n",
      "  (0, 2)\t1\n",
      "  (0, 19)\t1\n",
      "  (1, 17)\t3\n",
      "  (1, 27)\t2\n",
      "  (1, 22)\t1\n",
      "  (1, 7)\t1\n",
      "  (1, 8)\t1\n",
      "  (1, 3)\t1\n",
      "  (1, 9)\t1\n",
      "  (1, 34)\t1\n",
      "  (1, 16)\t1\n",
      "  (1, 33)\t1\n",
      "  (2, 17)\t1\n",
      "  (2, 15)\t1\n",
      "  (2, 11)\t1\n",
      "  (2, 0)\t1\n",
      "  (2, 20)\t1\n",
      "  (2, 4)\t4\n",
      "  (2, 24)\t1\n",
      "  (2, 5)\t3\n",
      "  (2, 1)\t1\n",
      "  (2, 28)\t2\n",
      "  (2, 13)\t1\n",
      "  (2, 29)\t1\n",
      "  (2, 10)\t1\n",
      "  (2, 14)\t1\n",
      "  (2, 32)\t1\n",
      "--------------------------------------------------\n",
      "[[0 0 1 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 1 1 0 2 0 1 0 2 1 0 0 0 1 1 0 0 0]\n",
      " [0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 3 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 1 1]\n",
      " [1 1 0 0 4 3 0 0 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 2 1 0 0 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "def hanzivec():\n",
    "    \"\"\"\n",
    "    中文特征值化\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    c1, c2, c3 = cutword() # jieba分词好的中文文本，就像结巴说出来的话\n",
    "    print('-'*50)\n",
    "    print(c1)\n",
    "    print(c2)\n",
    "    print(c3)\n",
    "    print('-'*50)\n",
    "\n",
    "    cv = CountVectorizer()\n",
    "\n",
    "    data = cv.fit_transform([c1, c2, c3])\n",
    "\n",
    "    print(cv.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "\n",
    "    print(data)\n",
    "    print('-'*50)\n",
    "\n",
    "    print(data.toarray())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "hanzivec()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-26T12:03:29.151944900Z",
     "start_time": "2024-05-26T12:03:29.101084300Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1.4 tf-idf\n",
    "### tf：该词在该文档中出现的次数/该文档的总词数\n",
    "### idf：lg(总文档数量/出现该词的文档数量)\n",
    "\n",
    "词频 (TF) 是一词语出现的次数除以该文件的总词语数。假如一篇文件的总词语数是100个，而词语“母牛”出现了3次，那么“母牛”一词在该文件中的词频就是3/100=0.03。一个计算文件频率 (IDF) 的方法是文件集里包含的文件总数除以测定有多少份文件出现过“母牛”一词。所以，如果“母牛”一词在1,000份文件出现过，而文件总数是10,000,000份的话，其逆向文件频率就是 lg(10,000,000 / 1,000)=4。最后的TF-IDF的分数为0.03 * 4=0.12。\n",
    "\n",
    "词频tf是对词数(term count)的归一化，以防止它偏向长的文件。（同一个词语在长文件里可能会比短文件有更高的词数，而不管该词语重要与否。\n",
    "平滑idf = log（N + 1/ N(x) + 1）\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 传给fit_transform的文本，英文可以直接传\n",
    "### 如果是中文，则需要先分词（上面的cut就是为了分词，变得像英文一样），无需再拼接。"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['今天', '很', '残酷', '，', '明天', '更', '残酷', '，', '后天', '很', '美好', '，', '但', '绝大部分', '是', '死', '在', '明天', '晚上', '，', '所以', '每个', '人', '不要', '放弃', '今天', '。']\n",
      "['我们', '看到', '的', '从', '很', '远', '星系', '来', '的', '光是在', '几百万年', '之前', '发出', '的', '，', '这样', '当', '我们', '看到', '宇宙', '时', '，', '我们', '是', '在', '看', '它', '的', '过去', '。']\n",
      "['如果', '只用', '一种', '方式', '了解', '某样', '事物', '，', '你', '就', '不会', '真正', '了解', '它', '。', '了解', '事物', '真正', '含义', '的', '秘密', '取决于', '如何', '将', '其', '与', '我们', '所', '了解', '的', '事物', '相', '联系', '。']\n",
      "--------------------------------------------------\n",
      "['今天 很 残酷 ， 明天 更 残酷 ， 后天 很 美好 ， 但 绝大部分 是 死 在 明天 晚上 ， 所以 每个 人 不要 放弃 今天 。', '我们 看到 的 从 很 远 星系 来 的 光是在 几百万年 之前 发出 的 ， 这样 当 我们 看到 宇宙 时 ， 我们 是 在 看 它 的 过去 。', '如果 只用 一种 方式 了解 某样 事物 ， 你 就 不会 真正 了解 它 。 了解 事物 真正 含义 的 秘密 取决于 如何 将 其 与 我们 所 了解 的 事物 相 联系 。']\n",
      "--------------------------------------------------\n",
      "['一种' '不会' '不要' '之前' '了解' '事物' '今天' '光是在' '几百万年' '发出' '取决于' '只用' '后天' '含义'\n",
      " '如何' '如果' '宇宙' '我们' '所以' '放弃' '方式' '明天' '星系' '晚上' '某样' '残酷' '每个' '看到'\n",
      " '真正' '秘密' '绝大部分' '美好' '联系' '过去' '这样']\n",
      "--------------------------------------------------\n",
      "  (0, 19)\t0.22360679774997896\n",
      "  (0, 2)\t0.22360679774997896\n",
      "  (0, 26)\t0.22360679774997896\n",
      "  (0, 18)\t0.22360679774997896\n",
      "  (0, 23)\t0.22360679774997896\n",
      "  (0, 30)\t0.22360679774997896\n",
      "  (0, 31)\t0.22360679774997896\n",
      "  (0, 12)\t0.22360679774997896\n",
      "  (0, 21)\t0.4472135954999579\n",
      "  (0, 25)\t0.4472135954999579\n",
      "  (0, 6)\t0.4472135954999579\n",
      "  (1, 33)\t0.24108220270067757\n",
      "  (1, 16)\t0.24108220270067757\n",
      "  (1, 34)\t0.24108220270067757\n",
      "  (1, 9)\t0.24108220270067757\n",
      "  (1, 3)\t0.24108220270067757\n",
      "  (1, 8)\t0.24108220270067757\n",
      "  (1, 7)\t0.24108220270067757\n",
      "  (1, 22)\t0.24108220270067757\n",
      "  (1, 27)\t0.48216440540135513\n",
      "  (1, 17)\t0.5500476874707075\n",
      "  (2, 32)\t0.15698297076974738\n",
      "  (2, 14)\t0.15698297076974738\n",
      "  (2, 10)\t0.15698297076974738\n",
      "  (2, 29)\t0.15698297076974738\n",
      "  (2, 13)\t0.15698297076974738\n",
      "  (2, 28)\t0.31396594153949475\n",
      "  (2, 1)\t0.15698297076974738\n",
      "  (2, 5)\t0.47094891230924213\n",
      "  (2, 24)\t0.15698297076974738\n",
      "  (2, 4)\t0.6279318830789895\n",
      "  (2, 20)\t0.15698297076974738\n",
      "  (2, 0)\t0.15698297076974738\n",
      "  (2, 11)\t0.15698297076974738\n",
      "  (2, 15)\t0.15698297076974738\n",
      "  (2, 17)\t0.11938959557761185\n",
      "--------------------------------------------------\n",
      "[[0.         0.         0.2236068  0.         0.         0.\n",
      "  0.4472136  0.         0.         0.         0.         0.\n",
      "  0.2236068  0.         0.         0.         0.         0.\n",
      "  0.2236068  0.2236068  0.         0.4472136  0.         0.2236068\n",
      "  0.         0.4472136  0.2236068  0.         0.         0.\n",
      "  0.2236068  0.2236068  0.         0.         0.        ]\n",
      " [0.         0.         0.         0.2410822  0.         0.\n",
      "  0.         0.2410822  0.2410822  0.2410822  0.         0.\n",
      "  0.         0.         0.         0.         0.2410822  0.55004769\n",
      "  0.         0.         0.         0.         0.2410822  0.\n",
      "  0.         0.         0.         0.48216441 0.         0.\n",
      "  0.         0.         0.         0.2410822  0.2410822 ]\n",
      " [0.15698297 0.15698297 0.         0.         0.62793188 0.47094891\n",
      "  0.         0.         0.         0.         0.15698297 0.15698297\n",
      "  0.         0.15698297 0.15698297 0.15698297 0.         0.1193896\n",
      "  0.         0.         0.15698297 0.         0.         0.\n",
      "  0.15698297 0.         0.         0.         0.31396594 0.15698297\n",
      "  0.         0.         0.15698297 0.         0.        ]]\n",
      "--------------------------------------------------\n",
      "[array(['放弃', '不要', '每个', '所以', '晚上', '绝大部分', '美好', '后天', '明天', '残酷', '今天'],\n",
      "      dtype='<U4'), array(['过去', '宇宙', '这样', '发出', '之前', '几百万年', '光是在', '星系', '看到', '我们'],\n",
      "      dtype='<U4'), array(['联系', '如何', '取决于', '秘密', '含义', '真正', '不会', '事物', '某样', '了解', '方式',\n",
      "       '一种', '只用', '如果', '我们'], dtype='<U4')]\n"
     ]
    }
   ],
   "source": [
    "# 规范{'l1'，'l2'}，默认='l2'\n",
    "# 每个输出行都有单位范数，或者：\n",
    "#\n",
    "# 'l2'：向量元素的平方和为 1。当应用 l2 范数时，两个向量之间的余弦相似度是它们的点积。\n",
    "#\n",
    "# 'l1'：向量元素的绝对值之和为 1。参见preprocessing.normalize。\n",
    "\n",
    "# smooth_idf布尔值，默认 = True\n",
    "# 通过在文档频率上加一来平滑 idf 权重，就好像看到一个额外的文档包含集合中的每个术语恰好一次。防止零分裂。\n",
    "# idf = log（N + 1/ N(x) + 1）\n",
    "# 比如训练集中有某个词，测试集中没有，就是生僻词，就会造成n(x)分母为零，log(n/n(x)),从而出现零分裂\n",
    "\n",
    "def tfidfvec():\n",
    "    \"\"\"\n",
    "    中文特征值化,计算tfidf值\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    c1, c2, c3 = cutword()\n",
    "\n",
    "    print(\"-\"*50)\n",
    "    print([c1, c2, c3])\n",
    "    print(\"-\"*50)\n",
    "\n",
    "    tf = TfidfVectorizer(smooth_idf=True)\n",
    "\n",
    "    data = tf.fit_transform([c1, c2, c3])\n",
    "    # data = tf.fit_transform(        [\"life is  short,i like python life\",\n",
    "    #      \"life is too long,i dislike python\",\n",
    "    #      \"life is short\"])\n",
    "\n",
    "    print(tf.get_feature_names_out())\n",
    "    print('-'*50)\n",
    "\n",
    "    print(data)\n",
    "    print('-'*50)\n",
    "\n",
    "    print(data.toarray())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "tfidfvec()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-26T12:56:17.882820800Z",
     "start_time": "2024-05-26T12:56:17.847105400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 2 特征处理，不同的特征拉到到同一个量纲"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         0.         0.         0.        ]\n",
      " [0.         1.         1.         0.83333333]\n",
      " [0.5        0.5        0.6        1.        ]]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "def mm():\n",
    "    \"\"\"\n",
    "    归一化处理\n",
    "    :return: NOne\n",
    "    \"\"\"\n",
    "    # 归一化缺点 容易受极值的影响\n",
    "    # feature_range代表归一化后的特征值范围，一般设置为(0,1),或者(-1,1),默认是(0,1)\n",
    "    mm = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "    data = mm.fit_transform([[90, 2, 10, 40], [60, 4, 15, 45], [75, 3, 13, 46]]) # 房子的信息\n",
    "\n",
    "    print(data)\n",
    "    print(type(data))\n",
    "\n",
    "    return None\n",
    "    #transform和fit_transform不同是，transform用于测试集，而且不会重新找最小值和最大值\n",
    "\n",
    "\n",
    "mm()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-08T08:40:15.989081100Z",
     "start_time": "2024-05-08T08:40:15.969046900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.06904497 -1.35873244  0.98058068]\n",
      " [-0.26726124  0.33968311  0.39223227]\n",
      " [ 1.33630621  1.01904933 -1.37281295]]\n",
      "<class 'numpy.ndarray'>\n",
      "--------------------------------------------------\n",
      "[2.33333333 3.         1.33333333]\n",
      "--------------------------------------------------\n",
      "[1.55555556 8.66666667 2.88888889]\n",
      "--------------------------------------------------\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "def stand():\n",
    "    \"\"\"\n",
    "    标准化缩放，不是标准正态分布，是均值为0，方差为1的均匀分布\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    std = StandardScaler()\n",
    "\n",
    "    data = std.fit_transform([[1., -1., 3.], [2., 4., 2.], [4., 6., -1.]])\n",
    "\n",
    "    # 计算的是特征之间的方差和均值\n",
    "    print(data)\n",
    "    print(type(data))\n",
    "    print('-' * 50)\n",
    "\n",
    "    print(std.mean_)\n",
    "    print('-' * 50)\n",
    "\n",
    "    print(std.var_)\n",
    "    print('-' * 50)\n",
    "\n",
    "    print(std.n_samples_seen_)  # 样本数\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "stand()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-08T08:40:17.920364Z",
     "start_time": "2024-05-08T08:40:17.892451500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.06904497 -1.35873244  0.98058068]\n",
      " [-0.26726124  0.33968311  0.39223227]\n",
      " [ 1.33630621  1.01904933 -1.37281295]]\n",
      "--------------------------------------------------\n",
      "[0. 0. 0.]\n",
      "[1.         1.         1.00000001]\n"
     ]
    }
   ],
   "source": [
    "std1 = StandardScaler()\n",
    "# 为了证明上面输出的结果的均值是为0的，方差为1\n",
    "# 注意上面的结果是ndarray，这里传给fit_transform的是list\n",
    "data1 = std1.fit_transform([[-1.06904497, -1.35873244, 0.98058068],\n",
    "                           [-0.26726124, 0.33968311, 0.39223227],\n",
    "                            [1.33630621, 1.01904933, -1.37281295]])\n",
    "\n",
    "print(data1)  # 和原来一样，说明这些数据已经是均值为0，方差为1的分布\n",
    "print('-' * 50)\n",
    "\n",
    "# 均值\n",
    "print(std1.mean_)\n",
    "# 方差\n",
    "print(std1.var_)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T14:32:08.696588400Z",
     "start_time": "2024-05-07T14:32:08.659113700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "1.5555556666666668"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.square(1-2.333)+np.square(2-2.333)+np.square(4-2.333))/3    # 计算方差"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T14:32:38.788860200Z",
     "start_time": "2024-05-07T14:32:38.761908400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "-1.068779614944516"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1-2.333)/np.sqrt(1.55555)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-07T14:32:59.066963200Z",
     "start_time": "2024-05-07T14:32:59.051196Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## transform和fit_transform不同是，transform用于测试集，\n",
    "### 不会重新找最小值和最大值,不会重新计算均值方差。而是用训练集的均值方差来进行标准化\n",
    "### 训练集和测试集，默认服从同一分布，才能做预测"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 3 缺失值处理\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         2.        ]\n",
      " [3.66666667 3.        ]\n",
      " [7.         6.        ]\n",
      " [3.         2.        ]]\n"
     ]
    }
   ],
   "source": [
    "# 删除，可以用pd和np\n",
    "# 下面是填补\n",
    "def im():\n",
    "    \"\"\"\n",
    "    缺失值处理\n",
    "    :return:NOne\n",
    "    \"\"\"\n",
    "    # NaN, nan,特征的缺失值必须是这种形式，如果是？号(或者其他符号)，就要replace换成这种\n",
    "    im = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "\n",
    "    data = im.fit_transform([[1, 2], [np.nan, 3], [7, 6], [3, 2]])\n",
    "\n",
    "    print(data)\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "im()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-07T14:47:02.688242200Z",
     "start_time": "2024-05-07T14:47:02.209395200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 4 降维\n",
    "# 降维就是特征数变少\n",
    "# 降维可以提高模型训练速度（模型的参数减少了）"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x1' 'x2']\n",
      "[[2 0]\n",
      " [1 4]\n",
      " [1 1]]\n",
      "The support is [1 2]\n"
     ]
    }
   ],
   "source": [
    "def var():\n",
    "    \"\"\"\n",
    "    特征选择-删除低方差的特征\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    #默认只删除方差为0,threshold是方差阈值，删除方差比这个值小的那些特征\n",
    "    var = VarianceThreshold(threshold=0.1)\n",
    "\n",
    "    data = var.fit_transform([[0, 2, 0, 3],\n",
    "                              [0, 1, 4, 3],\n",
    "                              [0, 1, 1, 3]])\n",
    "\n",
    "    print(var.get_feature_names_out())\n",
    "    print(data)\n",
    "\n",
    "    # 获得剩余的特征的列编号\n",
    "    print('The support is %s' % var.get_support(True))\n",
    "    return None\n",
    "\n",
    "\n",
    "var()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-08T08:47:36.297971200Z",
     "start_time": "2024-05-08T08:47:36.281978400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.88888889  4.66666667 13.55555556  8.22222222]\n",
      "29.333333333333336\n",
      "--------------------------------------------------\n",
      "[[ 1.28620952e-15  3.82970843e+00]\n",
      " [ 5.74456265e+00 -1.91485422e+00]\n",
      " [-5.74456265e+00 -1.91485422e+00]]\n",
      "--------------------------------------------------\n",
      "[22.          7.33333333]\n",
      "29.333333333333332\n",
      "--------------------------------------------------\n",
      "[0.75 0.25]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "def pca():\n",
    "    \"\"\"\n",
    "    主成分分析进行特征降维\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    # n_ components:小数 0~1 业界选择 90~95%\n",
    "\n",
    "    # 当n_components的值为0到1之间的浮点数时，表示我们希望保留的主成分解释的方差比例。\n",
    "    # 具体而言，n_components=0.9表示我们希望选择足够的主成分，以使它们解释数据方差的90%\n",
    "\n",
    "    # n_components如果是整数   减少到的特征数量\n",
    "\n",
    "    # 原始数据方差\n",
    "    print(np.var(np.array([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]]), axis=0))\n",
    "    print(np.var(np.array([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]]), axis=0).sum())\n",
    "    print('-'* 50)\n",
    "\n",
    "    pca = PCA(n_components=0.95)\n",
    "\n",
    "    data = pca.fit_transform([[2, 8, 4, 5], [6, 3, 0, 8], [5, 4, 9, 1]])\n",
    "\n",
    "    # 降维后的数据\n",
    "    print(data) # ndarray\n",
    "    print('-'*50)\n",
    "\n",
    "    #计算data的方差\n",
    "    print(np.var(data, axis=0))\n",
    "    print(np.var(data, axis=0).sum())\n",
    "    print('-'*50)\n",
    "\n",
    "\n",
    "    # 计算每个主成分的方差占总方差的比例\n",
    "    print(pca.explained_variance_ratio_)\n",
    "    # 计算data的方差占总方差的比例\n",
    "    print(pca.explained_variance_ratio_.sum())\n",
    "\n",
    "    return None\n",
    "\n",
    "\n",
    "pca()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-05-08T09:15:48.181369100Z",
     "start_time": "2024-05-08T09:15:48.155559Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "x = np.random.rand(10000) #每个的概率\n",
    "t = np.arange(len(x))\n",
    "plt.plot(t,x,'g.',label=\"Uniform Distribution\")\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.grid()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-08T09:21:57.888559700Z",
     "start_time": "2024-05-08T09:21:57.643337600Z"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
